For sequence-based [1]}, [2]} or video-based [3]}, [4]} FER, RNNs such as LSTM networks have been used to learn temporal information along with CNNs that exploit spatial information from the individual video frames [5]}, [6]}. Alternatively, 3D CNNs have also been explored for video-based FER [7]}, [5]}. For example, a 3D CNN was used with deformable action parts constraints for dynamic motion encoding in FER called CNN-DAP [7]}. In [5]}, a 3D CNN was used in an ensemble with two 2D CNN models. Unlike 3D CNNs that share weights in the time dimension, in [11]}, a method was proposed that used a sequence of images without weight sharing in the time dimension.
